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Efficient automatic scheduling of imaging and vision pipelines for the GPU
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1142/s0129626412500107openalex-snapshot
POLLY — PERFORMING POLYHEDRAL OPTIMIZATIONS ON A LOW-LEVEL INTERMEDIATE REPRESENTATION
inferredcites →https://doi.org/10.1145/1379022.1375595openalex-snapshot
A practical automatic polyhedral parallelizer and locality optimizer
inferredcites →https://doi.org/10.1145/2185520.2185528openalex-snapshot
Decoupling algorithms from schedules for easy optimization of image processing pipelines
inferredcites →https://doi.org/10.1145/2400682.2400713openalex-snapshot
Polyhedral parallel code generation for CUDA
inferredcites →https://doi.org/10.1145/2897824.2925952openalex-snapshot
Automatically scheduling halide image processing pipelines
inferredcites →https://doi.org/10.1145/3178487.3178507openalex-snapshot
An effective fusion and tile size model for optimizing image processing pipelines
inferredcites →https://doi.org/10.1145/3197517.3201383openalex-snapshot
Differentiable programming for image processing and deep learning in halide
inferredcites →https://doi.org/10.1145/3197978openalex-snapshot
A Survey on Compiler Autotuning using Machine Learning
inferredcites →https://doi.org/10.1145/3200691.3178507openalex-snapshot
An effective fusion and tile size model for optimizing image processing pipelines
inferredcites →https://doi.org/10.1145/3306346.3322967openalex-snapshot
Learning to optimize halide with tree search and random programs
inferredcites →https://doi.org/10.1145/3310248openalex-snapshot
Schedule Synthesis for Halide Pipelines through Reuse Analysis
inferredcites →https://doi.org/10.1145/3368826.3377928openalex-snapshot
NeuroVectorizer: end-to-end vectorization with deep reinforcement learning
inferredcites →https://doi.org/10.1145/3406117openalex-snapshot
Schedule Synthesis for Halide Pipelines on GPUs
inferredcites →https://doi.org/10.48550/arxiv.1802.04730openalex-snapshot
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
inferredcites →https://doi.org/10.48550/arxiv.1802.04799openalex-snapshot
TVM: An Automated End-to-End Optimizing Compiler for Deep Learning
inferredcites →https://doi.org/10.48550/arxiv.1805.08166openalex-snapshot
Learning to Optimize Tensor Programs
inferredcites →https://doi.org/10.48550/arxiv.2104.04955openalex-snapshot
A Deep Learning Based Cost Model for Automatic Code Optimization
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1007/978-3-031-16767-6_5openalex-snapshot
Building a Join Optimizer for Soufflé
inferred← citeshttps://doi.org/10.1007/978-3-031-30442-2_19openalex-snapshot
Shisha: Online Scheduling of CNN Pipelines on Heterogeneous Architectures
inferred← citeshttps://doi.org/10.1007/978-3-032-02436-7_13openalex-snapshot
PEAK: Generating High-Performance Schedules in MLIR
inferred← citeshttps://doi.org/10.1007/s10586-024-04728-5openalex-snapshot
A hierarchical data partitioning strategy for irregular applications: a case study in digital pathology
inferred← citeshttps://doi.org/10.1109/access.2025.3548105openalex-snapshot
Multi-Architecture Halide Template-Driven Automatic Library Function Generation for Simulink Models
inferred← citeshttps://doi.org/10.1109/mcg.2022.3149550openalex-snapshot
Differentiable Visual Computing: Challenges and Opportunities
inferred← citeshttps://doi.org/10.1145/3519939.3523442openalex-snapshot
Autoscheduling for sparse tensor algebra with an asymptotic cost model
inferred← citeshttps://doi.org/10.1145/3591236openalex-snapshot
Mosaic: An Interoperable Compiler for Tensor Algebra
inferred← citeshttps://doi.org/10.1145/3620666.3651383openalex-snapshot
Optimal Kernel Orchestration for Tensor Programs with Korch
inferred← citeshttps://doi.org/10.1145/3669940.3707218openalex-snapshot
Exo 2: Growing a Scheduling Language
inferred← citeshttps://doi.org/10.1145/3689741openalex-snapshot
CoolerSpace: A Language for Physically Correct and Computationally Efficient Color Programming
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